How One SaaS Workforce Unlocked AI Access for 300+

A UserTesting program lead piped Custom GPTs into Confluence and Highspot — standardizing AI access for 300+ employees and replacing scattered use with governed enterprise tooling.

300+

Enabled across 300+ employees

4–8 weeks

Implementation Time

Michael Domanic

Head of Generative AI Business Strategy

UserTesting

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The Challenge

UserTesting's employees were using AI tools inconsistently and in isolation — some teams had access, most didn't, and there was no shared infrastructure for capturing what was working. Knowledge stayed siloed. The company had the intent to become AI-enabled but lacked the internal program structure to move from scattered experimentation to coordinated, enterprise-wide capability across 300+ employees.

What They Built

Michael led an internal AI enablement program to bring generative AI adoption across UserTesting's full workforce. The program standardized on Enterprise ChatGPT and Custom GPTs tailored to specific functions, integrated AI into existing knowledge management tools like Confluence and Highspot, and built internal governance around responsible use. The focus was on embedding AI into workflows people already used — not creating a separate initiative that competed for attention — driving adoption through relevance rather than mandate.

Michael's approach centered on a single design principle: embed AI where work already happens, rather than asking employees to go somewhere new to use it. The program standardized on Enterprise ChatGPT as the core platform and built Custom GPTs tailored to specific business functions — giving different teams tools relevant to their actual work rather than generic access to a general assistant. AI was integrated directly into Confluence and Highspot — the knowledge management and sales enablement tools already in daily use — so employees encountered AI assistance at the point of need rather than having to context-switch to access it. Internal governance was built to define responsible use, manage data exposure, and establish clear boundaries. The focus throughout was on making AI useful before making it widespread — driving adoption through demonstrated relevance rather than top-down mandate. The result was enterprise-wide AI access and enablement across 300+ employees within a 2–4 month rollout.

AI Role

Enterprise ChatGPT and Custom GPTs serve as the primary AI layer, with Custom GPTs tailored to specific functions and workflows across the organization. The AI handles task-specific generation, drafting, and retrieval within workflows employees already use, with governance rules embedded to guide responsible use.

AI Model

ChatGPT

Infrastructure

Enterprise ChatGPT (primary AI platform) • Custom GPTs (function-specific configurations) • Confluence (knowledge management integration) • Highspot (sales enablement integration)

Integration Points

Enterprise ChatGPT connected to function-specific Custom GPT configurations • AI embedded into Confluence knowledge base at point of search and retrieval • Highspot integration enabling AI-assisted content access and recommendation • Internal governance layer governing data exposure across all connected systems

Impact

300+ Employees with AI Access

Generative AI access and enablement extended across the full UserTesting workforce through structured program rollout.

Governed Tool Stack Standardized

Moved from fragmented individual AI usage to standardized Enterprise ChatGPT and Custom GPTs with enterprise governance in place.

Knowledge Systems Integrated

AI embedded into existing Confluence and Highspot workflows, reducing adoption friction by meeting employees in their existing tools.

Technology Utilized

Implementation Complexity

Time Savings

Headcount Avoidance

The implementation builds on Enterprise ChatGPT and integrates with existing knowledge management tools like Confluence and Highspot — requiring meaningful program design, governance development, and Custom GPT configuration, but no proprietary model development or complex API engineering. Complexity is moderate, driven by organizational scope rather than technical depth.

Best Fit For

Internal AI champions and enablement leaders at mid-size SaaS companies; HR and L&D professionals designing enterprise AI adoption programs.

Michael Domanic

Head of Generative AI Business Strategy

UserTesting

Head of AI at UserTesting, specializing in generative AI strategy and conversational interfaces, and blending deep AI expertise with a passion for human-centered innovation

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Industry:

Technology & Software

Business Function:

HR & People

Product & Engineering

Company Size:

251-1,000

Project Cost:

Cost not disclosed

Ownership:

Public Company

Organization Type:

Public Company

AI Pattern:

AI Workforce Enablement

Knowledge Management & Search (RAG)

Value Type:

Time Savings

Headcount Avoidance

AI Model:

Frequently Asked Questions

How did UserTesting give 300+ employees governed AI access through workforce enablement?

The experts followed one design principle: embed AI where work already happens rather than asking people to go somewhere new. The program standardized on Enterprise ChatGPT, built function-specific Custom GPTs, and integrated AI directly into the knowledge management and sales enablement tools already in daily use, with governance defining responsible use. The result was AI access and enablement across UserTesting's 300-plus employees.

What AI tools and models were used at UserTesting?

The program standardized on Enterprise ChatGPT and Custom GPTs tailored to specific functions, and integrated AI into Confluence and Highspot, the tools employees already used. The approach combined AI workforce enablement with knowledge management and search (RAG).

What results did UserTesting achieve?

More than 300 employees gained governed AI access, the tool stack was standardized on Enterprise ChatGPT and Custom GPTs with enterprise governance in place, and AI was integrated into existing Confluence and Highspot workflows to reduce adoption friction.

How long did the rollout take?

Enterprise-wide access and enablement landed within a 2–4 month rollout.

Who is this AI enablement approach best for?

Internal AI champions and enablement leaders at mid-size SaaS companies, and HR and L&D professionals designing enterprise AI adoption programs.